Numerous immunoassays using the competitive binding principle have been developed to determine the amount of biologically or medically relevant chemicals or proteins (e.g., enzymes, hormones, blood proteins) in a sample. Processing of data and reporting of results in immunoassays is of obvious importance when determining the fundamental differences in the nature of dose-response relations for certain biologicals in a sample. Assay results are often calculated by determining where an analytical value (such as an absorbance) falls on a "standard curve" prepared using known concentrations of analytes.
Generally, assays of the competitive type (as contrasted with immunometric or reagent excess methods) give nonlinear dose-response curves. The shape of the curve is dictated by the mass-action principle and the affinties of antibody for analyte and label. As a result, construction of an accurate standard curve requires testing of multiple standards which requires significant technician time, rigorous quality control, and high consumption of generally costly reagents. While various mathematical models can improve the precision of curve fitting to obtain accurate results with less data required for a standard curve, mathematical fitting of nonlinear standard curves requires a computer and complex software or microprocessors interfaced with detection instruments, as well as extensive mathematical transformations of the data. Inaccuracy can result from poor fit of the mathematically fitted standard curve to the actual data, especially where nonlinear standard curves exhibit poor precision and sensitivity at or near analytically critical medical decision points.
Thus, a method for producing linear standard curves in competitive assays that (1) uses only two standards, (2) eliminates use of complex mathematical curve-fitting procedures, and (3) provides more consistent precision across the range of analyte concentrations, would be a great improvement over techniques currently in use. Such a method would present clear qualitative and linear quantitative information by a reproducible method of data processing and reduce the opportunity for errors that result from transformation of response curves (i.e. compression of errors with apparent improvement of data), errors from linear interpolation between points, or errors that result from assumption of linearity.